Support vector regression and multilayer feed forward neural networks for non-exercise prediction of VO2max

• Published in: Expert systems with applications 2009-08, Vol.36 (6), p.10112-10119
• Main Authors: Akay, Mehmet Fatih, Inan, Cigdem, Bradshaw, Danielle I., George, James D.
• Format: Article
• Language: English
• Subjects: Cardiorespiratory fitness; Multilayer feed forward neural networks; Support vector regression; VO2max
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2009.01.009

The purpose of this study is to develop non-exercise (N-Ex) VO2max prediction models by using support vector regression (SVR) and multilayer feed forward neural networks (MFFNN). VO2max values of 100 subjects (50 males and 50 females) are measured using a maximal graded exercise test. The variables; gender, age, body mass index (BMI), perceived functional ability (PFA) to walk, jog or run given distances and current physical activity rating (PA-R) are used to build two N-Ex prediction models. Using 10-fold cross validation on the dataset, standard error of estimates (SEE) and multiple correlation coefficients (R) of both models are calculated. The MFFNN-based model yields lower SEE (3.23mlkg−1min−1) whereas the SVR-based model yields higher R (0.93). Compared with the results of the other N-Ex prediction models in literature that are developed using multiple linear regression analysis, the reported values of SEE and R in this study are considerably more accurate. Therefore, the results suggest that SVR-based and MFFNN-based N-Ex prediction models can be valid predictors of VO2max for heterogeneous samples.